Fuzzy Inference Systems Optimization

TL;DR

This paper evaluates different optimization techniques—genetic algorithms, particle swarm optimization, and simulated annealing—for improving fuzzy inference systems, highlighting their performance variability depending on the context.

Contribution

It provides a comparative analysis of optimization methods for fuzzy inference systems, emphasizing their context-dependent effectiveness.

Findings

Performance varies with context for each optimization method.

Genetic algorithms, particle swarm, and simulated annealing have different strengths.

No single method is universally superior across all scenarios.

Abstract

This paper compares various optimization methods for fuzzy inference system optimization. The optimization methods compared are genetic algorithm, particle swarm optimization and simulated annealing. When these techniques were implemented it was observed that the performance of each technique within the fuzzy inference system classification was context dependent.